{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "多分类示例"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SVC(gamma=0.001)的分类结果\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.99      0.99        88\n",
            "           1       0.99      0.97      0.98        91\n",
            "           2       0.99      0.99      0.99        86\n",
            "           3       0.98      0.87      0.92        91\n",
            "           4       0.99      0.96      0.97        92\n",
            "           5       0.95      0.97      0.96        91\n",
            "           6       0.99      0.99      0.99        91\n",
            "           7       0.96      0.99      0.97        89\n",
            "           8       0.94      1.00      0.97        88\n",
            "           9       0.93      0.98      0.95        92\n",
            "\n",
            "    accuracy                           0.97       899\n",
            "   macro avg       0.97      0.97      0.97       899\n",
            "weighted avg       0.97      0.97      0.97       899\n",
            "\n",
            "\n",
            "混淆矩阵\n",
            "[[87  0  0  0  1  0  0  0  0  0]\n",
            " [ 0 88  1  0  0  0  0  0  1  1]\n",
            " [ 0  0 85  1  0  0  0  0  0  0]\n",
            " [ 0  0  0 79  0  3  0  4  5  0]\n",
            " [ 0  0  0  0 88  0  0  0  0  4]\n",
            " [ 0  0  0  0  0 88  1  0  0  2]\n",
            " [ 0  1  0  0  0  0 90  0  0  0]\n",
            " [ 0  0  0  0  0  1  0 88  0  0]\n",
            " [ 0  0  0  0  0  0  0  0 88  0]\n",
            " [ 0  0  0  1  0  1  0  0  0 90]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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RcalYFREREZFkqVgVERERkWSpWBURERGRZKlYFREREZFkqVgVERERkWSpWBURERGRZOXus7rN2xvc2Mf2e8WNrYhNplvX6DqH7/MnN3b3fUdEl/1X9ObB7dxYt1lVnMhmzL+qxo0tPO7GXGMeOu4/ovHOyx7PNW7KYj1PY71SAd66taMbW3bpDm6s7+jq9FndeoV/DlmwdpUbq+s/2Y1d+azf268xPX7+shtLuU9v+6lPuLEuHJZ73Pdr/L7WRfvVioPdWKxX5pUn+b0oAcad7fd/HHzqKDfWatZT0XErIdabsu1gf7m+Szq4sUPHjY6us/PUdM+pG47y++k+fP1NbmyPu85xY9vUrIyuc8Qv5rqxR0490I0V3YN11v5+rTDzKH+/7jvL316AT956rhvrdd3f3Fhsf/XoyqqIiIiIJEvFqoiIiIgkS8WqiIiIiCRLxaqIiIiIJEvFqoiIiIgkS8WqiIiIiCQrd+uq7V70m1Bd2vO3buyMs7/pxtoM9VsdNKb3Rem22NgS9fmZ3+znykP89kLjuvgtPuZcOTG6zmNGnOjGVk322zx1vr3YfWfBxEPdWPc/mBt7v3P8d8079rnWjQ39R7xlTTXEWi59ferH3VisZc31d/wkus7ax892Yz2X1UWXLdLbI/u7sVgLsD7fmpd7nT1/0zr3si3tznsGubFY+6kZy/aKjvvZTn92Y68O3dqN9Sm4VeCCWw/xY2sfdWNd7vPbTELaLdvavLDYjcVa3/X7/qtubO1ePaLrHPcLf9/a46xj3FifsdFhCxVruxbbrwAeGDTBjZ15nl/rteX1xie2CV1ZFREREZFkqVgVERERkWSpWBURERGRZKlYFREREZFkqVgVERERkWSpWBURERGRZOVuXbXh2Rfc2PCJ57uxi8//hRu77hW/HQnAkwem20olZv2yN93YMXV+u6WZtdOj4647wm8fxvhGp9WiYu0wZu3fzo3NPGqUG1t38d+j64zlq/eRZ7mxzrdHh21xbf7h79df/+4vc4879DG/PdXupz2de9yitVm+2o31bdMhuuwOk7at9HSq4m9HrnVjC4+7Jfe4tY+PcGM9I63FitZ74st+rMY/1mOtdgC+suA0N7b7tA8an1hBvnzIw27s9Ev/w411XvbRbfkYe12N/RxnPuW/TsRaXgEcU+ePG2uJVXQLsFgLqqP38dtxHdXe368AvnrG19xY+1mVPX/oyqqIiIiIJEvFqoiIiIgkS8WqiIiIiCRLxaqIiIiIJEvFqoiIiIgkS8WqiIiIiCTLQghFz0FEREREZLN0ZVVEREREkqViVURERESSpWJVRERERJKlYlVEREREkqViVURERESSVXaxamZnmtmxzVxmLzPbapPHvmFmO5c7n6IpHw0pHw0pHw0pHw0pHw0pHw0pHw0pHw39S+cjhFDWF9ADeA4YWvr/FcCngJ2Bw0qP1dV7vgEvA4fXe2xbYCGwTQXmcw6wFJgL9C53vI96PkrjjQBuq3YuUswHcCGwAvgHcL7ywceA6cADwPFbej7qjTkKuH1Lzwcwv3Q+XQq8saXnozTe9sAiYPctOR/At+rtG0uBd4FLttR8lMa6BHij9DVqC98/2gI3lXLxHDCwnPHKvrIaQlgMDAH2KD20FdAG2BMYXXrsvXqLfBKYDTxjZheZ2Rzgz0Br4P+Z2aNm9oyZdWzuXMxsP+A7wMHA14Gf5NiksqSUDwAz+zRwPdlOWXUp5cPM+gOnAgcBA4BLzWzXHJuVW2L56ARMBq4DrgJ+Zmadm79V+aWUj43MbCfg6rzLlyOlfJhZ+2xKoVvpq2e+rcovpXzU813gFyGEV8sYI5eU8hFC+EG9faMb8DTZL75Vk1I+zGxf4ChgN6A/8AMz65pjs3JLKR/A2UAHoBfZL/93l3PcbdX4U3xmdhDZC/8G4Eelh9cCAVjvLHY+cBbwRaB7COFQM5sGfCeE8Fw58wGGAneEEJYAS8ysi5l1CCGsKnPcJkkwH5DtJFcA+1VgrGZJMB+rgdM2vsiY2WtAd+AvZY7bJAnmYxfg4hDCzNL8/gL0BN4uc9wmSTAfG40HZlRorCZLMB8HkF0RKUSC+dg4p5OAvcodK+e6k8pHvbl9AvhbCOGZSo3ZhHWmlo+9gbkhhPXAX8zsdaAbsKzMcZskwXwMBy4MIawD5prZcqAf2bvezVbuldW2QEfgGrLqfeNjm/2zWGZ2OtCVrIA6B6gzs6eAI4CbzOw3znITzOyMJsynJ/Bsvf8vIfstp1pSywfAZ4G3mrwFlZVUPkIIz4QQ6krLdAd2BZ5v1haVJ7V8vBBCuNvMWpvZyaU5zWvmNpUjqXyUnjuY7DxyczO2o1JSy8dBwBFmtsTMXjGzzzRra8qXWj4AJgDvAJPMbLSZVfMdqxTzsdF5ZO/QVFNq+ZgHfMbMdjGzgUAXsttoqiW1fECWA0pXVHcDljdxuQ8p68pqCOEJ4AkzGwusKT28LfC+s8jvyQrIM4AfhBB+YWY1wFPAk8CNznrObeKUWpOdSDZaRXZ/UVUkmA9CCKG659MG604uH/V8D7ipWlfdIel8nEuWj/8oXRWoitTyYWbtgGvJrpz1aOp2VEpq+SC7B/FbIYRJZnY4MN3MHgghfNDE5cuSWj7MbADZ7UMXkL0b832yQmCz41ZaavnYyMx6kt2/O6s5y5UrwXzMI9svfkdWNP4whLAmvkjlJJiPycBPzGwvYBjwQgjhtSYu+yFlFauOfmQ3n2+/aSCE8KaZrQValxKzI9ll6D+R/SbQx8weB1aGEJr1ibaStzdZbzuyS+JFKjIfKSo8H2Z2PNlvjwfmHaOCCs9HCOFaM7ub7ET3QAjh5bxjVUCR+bgUuDOE8JKZVb1YdRSWjxDCHfW+n116W/MAYE6uLamMIvePw4DfhBDGA5hZB+BzVKlYdRR+/gBOA+4uY/lKKjIfI4GFIYRjzawV8ICZzQkhPJlzWyqhyPPHjWa2DPg42eeITs2/GZUtVv/NzF4iu9T7ItmB3YCZdQH+F5hnZo+SHeQ7kt3Xsa70/6+SJTePucBAYHLp7ZmDgcU5xypXCvlISRL5sOwDVbcAw6p5VXUzCs+HmfUB2ocQng0hvGFmT5Kd3IooVgvPB3AC0MXMvkn29lk7M9suhHBSzvHKUXg+Sm/1/bLe1aEeFPfLf+H5IHvXrv6y75N1FSlCCvnY6GTgy2WOUa4U8nE4pduoQggbzOxp4BCyq5TVlkI+CCFMNbN1wCDgnrzjQOWK1VZkBcCdwOzSW89tN31SCGG5mQ0ju0+wDdn9DGcBR5Pd8wLZJ8hakb3V0lz3Adea2R+AfYG3QghV+fDMJlLJRyqSyIeZbU12cF4dQijy6lAS+SD7cNlNlnVJaEfWxmpMjnHKlUQ+Qgi1G783s6OBkSGEkc0dpwKSyAdwJNDDzCaQ3de2hmI+cJVKPh4B/tOybhHvlsaekmOccqWSD0pX43qHEJ5t9MktJ5V8LASGm9lDQGeyK4llXU3MKZV8bHQRMC6EsNl7Z5sslN/XayfgA7Jenq8CB5Qe7wWcUPp+funfvmQtYR4j+7TtLcCjZPdV7AXcC2xN9tvJkfXWMQE4o4nz6U92UnkQ6Ffu9n3U81F6/kgK6BmZWj6AE8nuMavfG/DkLTUfpedeBPyV7GrqKVvy/rHJvI4u4phJKR9kvRlnAStL/+63Jeej9NwzgAVkH1q9newt1C05H8OA31Z7v0gxH2T3qU4h6+O9HLhiS85H6blDgIcqsm0VSE5rsiKgA05TcbLep0b2FuMFQJfS45eQXXYeQvaC+bXS4wOAfYs6AJQP5UP5UD6Uj+K/lA/lQ/lQPkIIWGkyhSrdjEwIoegPQyVB+WhI+WhI+WhI+WhI+WhI+WhI+WhI+Wgo1XwkUayKiIiIiGxO2X9uVURERESkpahYFREREZFkqVgVERERkWRF+6wObnVKrhtaW3fd2Y3Nv6rGjT0waEJ03K8sOM2NtR38euMT24wZG6Y0+W+R5s1HXqNfivdmn/ee/0d2Hhm8mxtbv+xNN1aNfLw9sr8bqz3neTe27MR20XFj25VXpfLRurafu9z88zu6sdgxMW9N1+h8xj78eTfW52f+X1VtNespN9acfEDLHDMvjz/cjd130jXRZc87dJgby7v/VGofyXtc7Nnen/e4Li82cWYfNmTwcDe2vs4ft+hz6r/q/hETe8198cLd3Vhj+Rg292w31vPkusYnthnVyMcbv651Yxue6+TGvnDSg9Fx/2fuQDfW90tzG5/YZlQjH0vHDnBjl4+5w41d858jouO2n/pEnulEefnQlVURERERSZaKVRERERFJlopVEREREUmWilURERERSZaKVRERERFJlopVEREREUlWtHVVXu9N8lsMLay9xY3tcdf50XGv+fQkN3bp2DPcWLfxj0XHLVKsXc3QDk9Hlx3awW8fM6TLgf6CLdDiqTkmXf5DNxZrx3TRaP9nDFBzWbHbFbOy7/Zu7MuHPOTGjr/HPyY2dFoXXefC4yLH2opz3FifWdFhqyLWiid2HvjVioNbZJ0t0RZtUx1GLHFjt9U87MYWrF3lxho7p3b/o98Jp31d5dvSVMOR/f2WSo21e6vGzzmvDUcd5MaO+rH/Gnddp6lurLF8fG//6W5sIn2iyxZp9Qq/Bmm/3wo39tJq/xwA8VaC59V+yY3FWr1Vww5DFruxoR3edWPfPt2PAbT3d62K05VVEREREUmWilURERERSZaKVRERERFJlopVEREREUmWilURERERSZaKVRERERFJVu7WVa1r+7mxmbV3ubHax0e4sT5jZ0fXObbT5/3gfmvcULfoqMUa9+07cy87atFAN1Z0q4yYWHuhRwbv5sYOn/5cdNwll+WdUctrP9VvAzRrqt9mZdux/u+Tl4/5ZXSdsZZGu0/7ILps0V6/YSc3tk/bZW7s+rNOiY57/ZyfuLGvLDjNjbUdHB22IhY9v4sbm9ZrWzc24bUT3Vi/778aXWfKrZpiYm2cbqu5zY3tffOY6Lg1pNvmcEXvbdzY5JcOcWOzTvbPL2/8uja6zkN7LIpEV0aXLVLP37R2Y1uN8dsxzVlcEx33Kysj54iEX3Pznlvq+k+Ojjukdrgbq3QNoiurIiIiIpIsFasiIiIikiwVqyIiIiKSLBWrIiIiIpIsFasiIiIikiwVqyIiIiKSrNytq1j+dq7Fdpjkt0loTKsV+afb0lp33dmNxdrwDO3wdAvMpnix1maTX/J/jj2X1bmxE3d8ObrOifRpfGIfMTsMWZx72fOGfMmNtap7Kve4lbLosgFubH7/G9zY3jdf4MZ6vxDfR/q26eDPJ9LepQ+vR8dtaUM7+O12htZOd2PTHomfbyfu+dE8Zt482G/HFNN7ylvR+Ppco1ZH59sfd2OdFvqtvN4e2d+NTT3kh9F1Dpt7thvbrXadGyu6dWLHxxa6sXuv/70bi7XWBGh3+ntuLOV9J9bCbnp/f9+ZHj9ceO+6991Ypdv96cqqiIiIiCRLxaqIiIiIJEvFqoiIiIgkS8WqiIiIiCRLxaqIiIiIJEvFqoiIiIgkK3cvqJUDeldyHh95a/fq4cYO7eG305m2ym8tE2tXA/DQPL89VF/mRpdtabHWJbtd4s871v6jsXzcHGkftn7Zm9FlU9XuvG3c2D73LosuW822Inm8X7Mm13JfOOlBN7bPiPytvnZ8xnIvWwmx9jIHLBqTa8xnLvBbgAFMzDVq8dZ0yrfcvTPuisavXO6fm6Zd+wk3FmsrVQ0zfnFbziX9Vm4Adf0nu7FRPx3oxpYcnnM6FdJ1ut9iKvYzbqy15vpIa8WUxV7/yvpZzdjBDW04ym+J1WpW81sn6sqqiIiIiCRLxaqIiIiIJEvFqoiIiIgkS8WqiIiIiCRLxaqIiIiIJEvFqoiIiIgkS8WqiIiIiCQrd5/Vjo8tzLXcB538+rhjpE8mQM2+f3VjW33X7/dVDbG+YbE+ZleO/IIbG3plvAviA4MmuLGv8/HoskWK9WBdOnaAG1uw9tH4uB/RXqoxsVydN+RL0WVvuvdWN3bmsG+6sfZTn2h8YhWw90WL3FhtpxFubOohN7uxvm3ifSNjfY2L7pUZ23+7jfdjb4/sn3udle6FWC3TR10difr7QO/7z8q9zvHfvtONTby9T+5xm6p15PVxj7vOcWNH9vd7g564Y/xnfP5vT3dju0/7wI21It1955HBu7mxPtPnRZddMrXSs/loWzW5uxvrcPESN9Z2VvPXpSurIiIiIpIsFasiIiIikiwVqyIiIiKSLBWrIiIiIpIsFasiIiIikiwVqyIiIiKSrNytq2JtVkYtGujGas953o3NOaEmvtJ3/FDPhNusxGy9YkPuZeet6VrBmVTPglsPcWMLj7vBX25t/nFbrfB39X63vB0fuAJibWeWH7+HG/ugs7mx4aMejK4z1sbpnV6t3Vj76KiVEzuH9DzZj53XdZgbu/ep30fX+e1nT/TXid/ipxry7iOTLv+hG7ty+cHRdabcnirmq2d8zY/dMiX3uHtfs9KNDT3uXTd2c22/3Otsqtjx0mesH5vz61o3tmf7eLu/PmNnNz6xgsSOlzmLd3Jju3VZ58ZO3PGx6Don0vItylpCLFcrB/TOPe7KXv7r05za6W5sSO3wZq9LV1ZFREREJFkqVkVEREQkWSpWRURERCRZKlZFREREJFkqVkVEREQkWSpWRURERCRZuVtXxSw7sZ0be/0Gv6XEiD3nRsed+fUBueeUqo6PLXRjVy6Pt0MZ1+VFN3ZzpFVFrAVKNfT52Xo3dsyufmuhRc/vEh33y4NmurGXVvv5ePmP+0THrYgund1QrJ1bOY6p83PZbXy8RUvKYueQBWtXRZfdYdK2lZ5OxcRayMTaU8ValD1y6oGNrNU/h6Qs1nLroslnuLEHRl0dHbfvcX4uY8dT27p087jzdn7Lrf+Z67eZBOhL/DW5SLHXsZ2382uQs6f5Lf+u/N4XouvszOONTyxBC0f7Lbfmn+23i2xMrEaJHS/tlje/XaSurIqIiIhIslSsioiIiEiyVKyKiIiISLJUrIqIiIhIslSsioiIiEiyVKyKiIiISLIshFD0HERERERENktXVkVEREQkWSpWRURERCRZKlZFREREJFkqVkVEREQkWSpWRURERCRZZRerZnammR3bzGX2MrOtNnnsG2a2c7nzKZry0ZDy0ZDy0ZDy0ZDy0ZDy0ZDy0ZDy0dC/dD5CCGV9AT2A54Chpf9fAXwK2Bk4rPRYXb3nG/AycHi9x7YFFgLbVGA+5wBLgblA73LH+6jnozTeCOC2aucixXwAFwIrgH8A5ysffAyYDjwAHL+l56PemKOA27f0fADzS+fTpcAbW3o+SuNtDywCdt+S8wF8q96+sRR4F7hkS81HaaxLgDdKX6O28P2jLXBTKRfPAQPLGa/sK6shhMXAEGCP0kNbAW2APYHRpcfeq7fIJ4HZwDNmdpGZzQH+DLQG/p+ZPWpmz5hZx+bOxcz2A74DHAx8HfhJjk0qS0r5ADCzTwPXk+2UVZdSPsysP3AqcBAwALjUzHbNsVm5JZaPTsBk4DrgKuBnZta5+VuVX0r52MjMdgKuzrt8OVLKh5m1z6YUupW+eubbqvxSykc93wV+EUJ4tYwxckkpHyGEH9TbN7oBT5P94ls1KeXDzPYFjgJ2A/oDPzCzrjk2K7eU8gGcDXQAepH98n93OcfdVo0/xWdmB5G98G8AflR6eC0QgPXOYucDZwFfBLqHEA41s2nAd0IIz5UzH2AocEcIYQmwxMy6mFmHEMKqMsdtkgTzAdlOcgWwXwXGapYE87EaOG3ji4yZvQZ0B/5S5rhNkmA+dgEuDiHMLM3vL0BP4O0yx22SBPOx0XhgRoXGarIE83EA2RWRQiSYj41zOgnYq9yxcq47qXzUm9sngL+FEJ6p1JhNWGdq+dgbmBtCWA/8xcxeB7oBy8oct0kSzMdw4MIQwjpgrpktB/qRvevdbOVeWW0LdASuIaveNz622T+LZWanA13JCqhzgDozewo4ArjJzH7jLDfBzM5ownx6As/W+/8Sst9yqiW1fAB8FniryVtQWUnlI4TwTAihrrRMd2BX4PlmbVF5UsvHCyGEu82stZmdXJrTvGZuUzmSykfpuYPJziM3N2M7KiW1fBwEHGFmS8zsFTP7TLO2pnyp5QNgAvAOMMnMRptZNd+xSjEfG51H9g5NNaWWj3nAZ8xsFzMbCHQhu42mWlLLB2Q5oHRFdTdgeROX+5CyrqyGEJ4AnjCzscCa0sPbAu87i/yerIA8A/hBCOEXZlYDPAU8CdzorOfcJk6pNdmJZKNVZPcXVUWC+SCEEKp7Pm2w7uTyUc/3gJuqddUdks7HuWT5+I/SVYGqSC0fZtYOuJbsylmPpm5HpaSWD7J7EL8VQphkZocD083sgRDCB01cviyp5cPMBpDdPnQB2bsx3ycrBDY7bqWllo+NzKwn2f27s5qzXLkSzMc8sv3id2RF4w9DCGvii1ROgvmYDPzEzPYChgEvhBBea+KyH1JWseroR3bz+fabBkIIb5rZWqB1KTE7kl2G/hPZbwJ9zOxxYGUIoVmfaCt5e5P1tiO7JF6kIvORosLzYWbHk/32eGDeMSqo8HyEEK41s7vJTnQPhBBezjtWBRSZj0uBO0MIL5lZ1YtVR2H5CCHcUe/72aW3NQ8A5uTaksoocv84DPhNCGE8gJl1AD5HlYpVR+HnD+A04O4ylq+kIvMxElgYQjjWzFoBD5jZnBDCkzm3pRKKPH/caGbLgI+TfY7o1PybUdli9d/M7CWyS70vkh3YDZhZF+B/gXlm9ijZQb4j2X0d60r//ypZcvOYCwwEJpfenjkYWJxzrHKlkI+UJJEPyz5QdQswrJpXVTej8HyYWR+gfQjh2RDCG2b2JNnJrYhitfB8ACcAXczsm2Rvn7Uzs+1CCCflHK8cheej9FbfL+tdHepBcb/8F54Psnft6i/7PllXkSKkkI+NTga+XOYY5UohH4dTuo0qhLDBzJ4GDiG7SlltKeSDEMJUM1sHDALuyTsOVK5YbUVWANwJzC699dx20yeFEJab2TCy+wTbkN3PcBZwNNk9L5B9gqwV2VstzXUfcK2Z/QHYF3grhFCVD89sIpV8pCKJfJjZ1mQH59UhhCKvDiWRD7IPl91kWZeEdmRtrMbkGKdcSeQjhFC78XszOxoYGUIY2dxxKiCJfABHAj3MbALZfW1rKOYDV6nk4xHgPy3rFvFuaewpOcYpVyr5oHQ1rncI4dlGn9xyUsnHQmC4mT0EdCa7kljW1cScUsnHRhcB40IIm713tslC+X29dgI+IOvl+SpwQOnxXsAJpe/nl/7tS9YS5jGyT9veAjxKdl/FXsC9wNZkv50cWW8dE4Azmjif/mQnlQeBfuVu30c9H6Xnj6SAnpGp5QM4kewes/q9AU/eUvNReu5FwF/JrqaesiXvH5vM6+gijpmU8kHWm3EWsLL0735bcj5Kzz0DWED2odXbyd5C3ZLzMQz4bbX3ixTzQXaf6hSyPt7LgSu25HyUnjsEeKgi21aB5LQmKwI64DQVJ+t9amRvMV4AdCk9fgnZZechZC+YXys9PgDYt6gDQPlQPpQP5UP5KP5L+VA+lA/lI4SAlSZTqNLNyIQQiv4wVBKUj4aUj4aUj4aUj4aUj4aUj4aUj4aUj4ZSzUcSxaqIiIiIyOaU/edWRURERERaiopVEREREUmWilURERERSVa0z+rgVqfkuqF1w1EHubFjfvyYG5uxbK/ouEsf9f+oTM1l/rgxMzZMafLfIs2bj5jWtf3c2L0z7so97pDBw93Y+roX3Vg18vHy+MPd2CvD/T8Gc+i40dFxO9/+eJ7pRFUqH1t16+ouN+/S3dzYiP7+Nn1353jLy7o177mxsy84z41tO+UJN9acfEDLHDMx3Wd3jMaXndndjcWOi5hqHDOx7frj47VurDFtV/jXK1I+p7buurMbO2LG625sn3bxvxFz/VmnuLFWs55qfGKbUfRrTOx8e82nJ+Ue9+ahn3JjRb/GvPFr/5jYebt33di6G7pFx20/1T835lWNfCy49RA39uVDHnZjd94zKDpu3nNEjJcPXVkVERERkWSpWBURERGRZKlYFREREZFkqVgVERERkWSpWBURERGRZEW7AeS17uK/u7FxXfxPCcZiAEQ+9DpkSr5PvxdtZd/ti55Ci4h9WvfMQTPd2DF1J7qxOVdOjK7zk7cf2Oi8ivL+Pj3d2Mf2e8WNTX68vx/Dj0G8k8Ax3/Y/xfnklNbRcYu2dOwANzZ6xzuiy968vF2lp1MxsS4qt9Xc5i9Y43+aN3Y8QbzDSspevHB3N3Zvl9+7sSuX+91XANosX+3G1jc+rSRtu8i/JvXtZ+P7x9RDbnZj7133vhtrO7jxeRXlpr4/d2O/uvzg+MKX+6FHBvtdXdYve7OxabWoo/fJV3eNOzteO1Wz7tKVVRERERFJlopVEREREUmWilURERERSZaKVRERERFJlopVEREREUmWilURERERSVbu1lWrhx3mxh6uvcmN9b7/LDe290WLous8+xG/3U7RYq2aVg7o7cb6fGte7nWOWjTQjRXdrivWqmPyS4e4sZ4n17mxK5+Nt52Jtf5pNeup6LItbas//MmNrfiDv1xf3nJjrfbfK7rOfT/xhhu75J7Pu7He+C2vqiV2fnnmghvc2N43j4mOu+bCDW6sz9hi28vE2iZNW7WtG7v5CL+VV9tlr0fXWUM8XqTWtf7x/srwG3ONOfPrfq4AWtUVe55oCd3GR143x8eXnfdSVzf25jv+Puk36quO9xd1dGNf2e40N9Z2cPx4GP3Sy27svgFHu7H2U4s9t8TE2rk11k508bE7urFu/kt5LrqyKiIiIiLJUrEqIiIiIslSsSoiIiIiyVKxKiIiIiLJUrEqIiIiIslSsSoiIiIiycrduiqvvl+a68bWN7Ls0A7vurGbc86nUhaO7uPG5p/tt9rZEnX/UVs3FmsBNq7L76PjPvLCbm6ssX0rVbH2VPfd/8voshe/uZ8b23PCq25sXePTanHHX/6QG4u1v+t7Wby93QNLnnZjByzy215F2/9UyPKP7ZBvueP3cGOdb0+3ZU5j1nZpn2u5WEu/Ni8sji6b8nki1srrxbM6u7Gaff/qxnp1/Ht0nbHX3PMj7aGK1mfsbDd21LPvubHPvv7n6Li/WnGwG2s/9YnGJ1aQP9/lvxbcFmkF2JjLx9zhxiaO92uiPHRlVURERESSpWJVRERERJKlYlVEREREkqViVURERESSpWJVRERERJKlYlVEREREkpW7dVW0TcP1fijWmmj9sniblVhLkljrjj5jo8NWRE2kZc7gmaPc2JJvrHFjdf0nR9d54o5PubGJVLZtRCW1muXPe/6th7ix2M8fGt9/Poo2PPuCG/vlSn+fB7jvhiPcWJelj+eeU6UsHTvAjY3r4rdTmdzJ30fWzPDbl2WediPVaE8V0+VJv43QPm2XubE5V050Y72P9Nt8QbyVYNFW9N4m13Kx8+KVx38humzKrb5ir3GvDL+xijPJtF1R7LWuWC3x3qR2bmxcl+mRUTtE1zlrf3/clMXObZ8cf6Abi7VLA7h3xl1u7PqjDnJjsRrAXabZS4iIiIiIVImKVRERERFJlopVEREREUmWilURERERSZaKVRERERFJlopVEREREUmWilURERERSVbuPqsxC9aucmPzr6pxY32/FO9xt2d7P/7nRfs1PrGCxHqKvT/0cH/B/vFxp7/l9zGDlfGFC7Qh0n/tgUET3Njpl/5HdNzOFN87tJq+86cTo/Erzv+lG7vt5sb6kba8WO+/Q98e7S94wjtuaHBXvy9t6tbXvejGvr7bx91YrBfiwhm3RNc5pHZ4rvlUQ6eF7+dabmiHd/1YpCctAFf6oQOuHuPGqtGjt8/Y2W7sk2MPzDVmY32Jb+r7czfWe+LLbmx9rtlUTmzeg0/9mht7+Yuto+O2//V7bqznyXWNT+wjprFzQO/7/T7O7b8RydWs5s9FV1ZFREREJFkqVkVEREQkWSpWRURERCRZKlZFREREJFkqVkVEREQkWSpWRURERCRZLdK66pMPnuvGxg/02+mMvfXz0XFHt/OX7fHzdNtoxGy7KP/vC7fVPOzGUm5J89VbpuRa7q0DQjw+3m8Dtvu0D9xYrLVYNSy8yu9RtnZ7f+/97QC/zRdAbdt2buw2im9dFdP5dr8NWefb/eXuGjsoOu64C4rd9/NaPewwN7bkSMs97sq+27ux9gV34mnzwmI3Nm3Vtm4s1rqqHAcPf86NLRnfIqusiAW3HuLGFtbGW5vtffMFbqxmWcu364pZv8xvZTlvTVc39ubB/nnxgUFXR9c5bO7ZjU8sQbF2kW8c4+fj8OP8fR5g9I5+TRY7DmP1iUdXVkVEREQkWSpWRURERCRZKlZFREREJFkqVkVEREQkWSpWRURERCRZKlZFREREJFkt0rpq72tWurHp+/gtFL58iN+KCeCa/xzhxtove6LxiSWox+/fcmOjhg+MLnvRLve7sZRb0lw0+Qw3FmuVcWT//BPfc5Df5uSRwcW2cZp66rVuLNZ+6nOvfjo67soxO0WiLzQ2rY+kWHshgD3uOseN9WF2padTMVuNWerGrun1oBvrff9Z0XH7Tk33vBlrTXT9Wae4sek/8NsYxs6ZEG95NPv+/dxYDcW2cYo5eh+/XduVy/tFl+098aPZEvL8357uxq4Zc4cbO/E2v1UXfHTzUU57qpjpb/n13ITXdnBjbXO0z9SVVRERERFJlopVEREREUmWilURERERSZaKVRERERFJlopVEREREUmWilURERERSZaFEIqeg4iIiIjIZunKqoiIiIgkS8WqiIiIiCRLxaqIiIiIJEvFqoiIiIgkS8WqiIiIiCRLxaqIiIiIJKvixaqZtTWz1pt5zErfD2zCGOPMbKtKz60IykdDykdDykdDykdDykdDykdDykdDykdD/0r5KKvPqpl1B+4F3gE6Au8Cs4CjgF2BNcASYGvg86XvfwOsBu4Hzip93x64A7gBCKXndQ85JmdmlwBnl/77nRDCbTk3r9mUjw+tO7l81JvbKOCoEMLIvGPkWGdS+TCztsCPgU8BbwNjQggPl7WRzVt/Uvkozekc4DLgDeCUEMLC3BvY/HWnmA+dP/45Hx0vH56T9o8Pz2sE8O8hhFF5ty3nepPLh5ldCFxUGueKEMI1uTcwhFDWF9AJ2Ad4HOhf7/EfA0M28/xWwCDga8BIYLvSvxcCk4EjgDdyzmVf4EGgNdkP502ga7nbqHz8a+Sj3jp2ApYDt1czF6nlozTmJGAr4BDgr0DHLTgf+wGLge5Af+B3W/j+ofNHw7F1vGj/aGw+nwb+QQGvLanlg+wc+gywe2lO7wC75t22StwG0BN4hOzE/riZHWhmjwGfA64ws0fM7DoAM+sGHBtCeLDe8nWlBAOsBdaVMZe9gbkhhPUhhL8ArwPdyhgvD+WjoZTysdF4YEYFxskjpXwMByaGENaFEOaSFfD9yhgvj5TyMRS4I4SwJITwONDFzDqUMV4eKeVD54+GdLw0pP3jw0YBV5Q5RjlSysdq4LQQwqshhHnAa2QXAnIp6z6E0n0ML5Kd5LuUHm4LvAr8O1niWgE/KMXaAf9tZr3qDfM+WcXdLrKeCcCfQgh3NDKlecB/lX4YfUpzmt/kDSqT8tFQgvnAzAaX1ns58MWmb035UszHxnmYWUdgN7IX4KpIMB89gYfq/X8JWU7mNboxFZBgPnT++DAdL/+k/ePDPkuVX1c2Si0fIYRn6i3Tnezq+/NN3qBNlHtl9RNkVfxk4Ltm9hbZpV/ILvv+uPR9AAjZ/V+DgNn1xli/Me4JIZzbxB1lHvAX4HfArcAPQwhrmrYpFaF8NJRUPsysHXAt8OXGxmwhSeWjNI+fmNm3yK40vxBCeK1pm1IRqeWjNdmJeqNVwPZNWK5SUsuHzh8N6XhpSPvHh59bxOvKRsnlo57vATeFEFY1c7n/U1axGkL4fQjhcLLkjANeAf5cCncgq+g3XeZvwLHAtoDVC1Xi02YjgYUhhIPJ3p4ZamYfq8C4TaJ8NJRgPi4F7gwhvFSBsZottXyEEG4EvkF2D+/B/PM37qpILR9kH5rZvt7/2wEbKjBukySYj5Ho/FF/bB0vDY1E+0cyUs2HmR1Pdu9rWbdHVLR1VQjhULJPnAHUkiUL6m24mfUHziC7AbgV2Qca/kT29ltroE0ZUzic0lt2IYQNwNNkN8IXQvloKIF8nACcb2ZLgXuA4WZ2TxnjlSWBfBBCmEr2idE6spwUJoF8zKV0JcLMjKwgWVzGeGVJIB86f3x4Djpe/kn7R8JSyIeZ7QrcAowo56oqtMwfBVhG1g7h34EFwHvAT+vFzydrYTCL7NL088AlZJei72Az1X8zLCQrQA4ws6OBUynjHokKUT4aKiwfIYTaEELXEEI34CTgrhDCSXnHq5Ai94+NLgLGFfwW1kZF5uM+YJiZnQR8B3grZB8cKZLOHw3peGlI+0dDKewfKSksH2a2NfC/wNUhhDl5x9moUsXqNvzzPojXSv/fiazP11RKN12XPtxyADDFzPqRfULtErKbev8rhDAjhLCYTap5M5tgZmc0YR7Xk/1m8EfgV8BtoYp98OpRPhpKJR+pSCYfZjYEWBNCuK8C25VXEvkIIbxD9gGJb5L1JhxRiY3LIYl8oPPHh+h4aUD7R5pSycdxwIHAhWa2tPR1cu6tCuX39ZoKPEf2SbPtgSlk1XTnUnx/4AVgj1JiPlt6/EHg1NL3nYEfkRXPdcDMcudV1JfyoXwoH8qH8qF8KB/Kh/JRua+y/oLV5pjZ3iGE+Zs8tlPIbuSt/1ibEMLazSzfPoSwuqKTKpDy0ZDy0ZDy0ZDy0ZDy0ZDy0ZDy0ZDy0dC/Uj4qXqyKiIiIiFRKS3zASkRERESkIlSsioiIiEiyoo1fB7c6Jdc9Aq277uzG3pvk/hUvzu31oBsD2KftMjf21TO+5sZazXrKjc3YMMXc4Cby5mPp2AFu7PIx/h+CGNrh3ei4h44b7cY63/544xPbjGrkI2b1sMPc2E+vuza67CnXXeDGuo1/LNd8qpGP2PEy/6oaN/bAoAnRcb+y4DQ3ttV3d3BjlTpeoGX2kdEvvezGpr91UHTZZWf6f5p6fd2LueZT9DHzxq9r3diIPedGl5127SfcWMrnkNh5YqsxS91Yr45/j4574o7+vj9xzz6NT2wzqnIOqe3nxl48q7Mba7sifr2q5rJ8582YauQjdkxMPeRmN3beocOi465f9mae6URVKh+x15Hr5kx1Y33bdHBjtY/HG6L0PLkuGs/Dy4eurIqIiIhIslSsioiIiEiyVKyKiIiISLJUrIqIiIhIslSsioiIiEiyVKyKiIiISLKiratiYm0Sjpjxuhsb18VvDzNq0cDoOofWPOzGXh26tRvrMys6bEXEWoc8c8ENLbLOlb38jhd+s5K0HX/5Q27sVysOji7b4+d+S6P1eSdUBV2nv+fG5s/zl2uszQqT/NCbB/st5LpV4Xgpx6U3nOHG3q3ZEF32mml+Um4+wm8x1xItaza14Si/7dZXb5nixoZ2eNqNNXZOnXT5D93Y12//eHTZIsXa2F311+PcWKw1FcTbBV4TaZfVfuoT0XFb2uJjd3Rj9510tRuLtS0CGDJluBvL2+qtGnbezv85NrbNH1UrB/R2Y7Ft7n3/WW5s4XG3RNc5cNhX3FiljwldWRURERGRZKlYFREREZFkqVgVERERkWSpWBURERGRZKlYFREREZFkqVgVERERkWTlbl314oW7u7F7u/zejQ38qt/q4J1ereMrvcBvXbXjM34bp6ItWLvKjcVaSsSWA+g95S03lnKrpkWX+S2CxnXx23wNGey3UQFYvyzdVioxe7b32yLNXrSfG1t8Wp/ouM/U+rnc4/lzGp9YgRbceogb67XrYjfWbbDfNg9g3qAebmz58Xu4sc63t3zrqiXfWOPGYi2V9rjL/1nW7PvX6Dr71nw02/jEzpsPzfPbCN52nP8aAnDlcn/Zbf7u/3yqYelY/7w55Ty/PdVXFpzmxmbWTo+uc2Xf7d1Y+7rooi0u1j6zse36V9RxwT/cWKyF3U5/bOMP6neBA6p7TOjKqoiIiIgkS8WqiIiIiCRLxaqIiIiIJEvFqoiIiIgkS8WqiIiIiCRLxaqIiIiIJCt366pYq6gFJ/ktl7Yas9SNDe/6QnSd01Zt68Y63/54dNmWtr7Ob5t0ynUXuLFnLvDbCzVm4Sk7urGagtuKrB52mBubPspvs7LHXee7sT51s6PrfHtkfzdW9P4Rc9+lR7uxVqevcGMH91gUHTfW+qz7H0Oj82pprWv9NkELj7vFjcVaNfUh3rrqpw8e4wcP8HPSOTpqZby/qKMbm7a/f+6LnYvP/fSD0XX2vv8sN9aXudFli3TouNFu7Ohznndjx9SdGB23V8e/u7E3jmnnxmpmRYetiK3f9vfPWCuvN9/x953GdHxsoRsruj3i+mV+O7nYOeKV4Te6sVj7OqhOC7u8YjXIksP95T4Ym27bz/p0ZVVEREREkqViVURERESSpWJVRERERJKlYlVEREREkqViVURERESSpWJVRERERJKVu3VVrBXQeU9+yY31+ukSNzaui996AeDK5X6rm5RdPuYON1b7+Ag3ttUjnaLjzo+0vRo8c5QbazXrqei4lRBrUTZvTVc31u/7r7qxxWMHRNcZawN2KH6rm6LbWrWf+kQk5i/nH0mZq2Yf58be6dXaX2cj41bK8o/t4MZix3qfsfEWZnm1XVHs7+6x7bpoxRlu7Kpv++eXoR3eja7zyj+2aXxiCYods0tu95dry8rouH8cH+nx02lDI7NqWV2e9NtqxdrU1fWf7MYae02NtYdKWexYmvZpv5XXqhPeiY7b+fa8MypW6647u7Ep5/mtJK9cfnB03GrUEv+3rqqtSURERESkmVSsioiIiEiyVKyKiIiISLJUrIqIiIhIslSsioiIiEiyVKyKiIiISLJUrIqIiIhIsnL3WY1ZX+f3S92z/Xu5x431Yb1r7Bg31m38Y7nX2VSxPmZDOzztxq7/UVs31mpWI/O+wA+9cUw7N1YzKz5sU8W2eWbtdDc2bZXf567rdH//uLfG76PamE4L38+9bCW8HOnfGOvvWXNZ/n33ol3ud2Onv71v7nEr5YPOVvV11uz7Vze27oZuVZxJ88T2gwkfH+TGhkaOQ4A5V050Y0PuO9aNfVT7bzZmmxq/D+uG5+J9r1ta7HX1vCF+b/N7Z9zlxl5a7Z/DARbcGul3/LP1bqya/Teb69vPnujGvrd//HiZSJ9KT6cqXrxwdzfWt00HN3bfpUdHx22P3yO80nRlVURERESSpWJVRERERJKlYlVEREREkqViVURERESSpWJVRERERJKlYlVEREREktUiratiYu2nyrGm2K4i0VYutY+PcGM7X/x3f9CLd4uuc8HaR91Yz5n5W4Q1Vd5tPrTHIjc2Z3GNv8JIqLF19iy4lUq/77/qxmLtuubsV+vGdpjktwAD6NvmaTdWdCsviLeUe2m431LnjV/7O8JWj8RPBJf3usONTZz60WxLc26vB1tk3JUDerux9lNbvnXV0rED3NjwUf42x9rttJ8ab7UTa110zST//FK0lX23d2OxVoFLDvdbdQEcPdt/vT7xFv+cevMR/s+uGt4e2d+N7bzdEje2T9tl0XGXjj3DjW33mt/Kq+NjC6PjVkLrWr/N2CvDb3RjVy73l2vM6mGHubFt/r7GjeVpbaYrqyIiIiKSLBWrIiIiIpIsFasiIiIikiwVqyIiIiKSLBWrIiIiIpIsFasiIiIikqyqt646pu5ENza46wvRZV9a7bez6T3xZTfmN5Soju4/auvGvnqL34JlaId3o+MecPUFbqzbLL8tUDX0PLnOjfmNQ2CrsZHWQ343EgB2u2SdGyt6H4i1+VpyuL/ciGfnurFx18fbwO1x1zlurM+s2dFli7bsxHZubOdJ/nFx7hi/9RDARZP91jM1FHvM5DXhtUFubGhtPB+x83FjbZ5aWqy12Z2d/G3+wuX+ObWxYybW5inWfqjo80tsbtPfOsiNxdrAASy63z8fL7ks1vaq5Vubxaw64R03Nid6THSIjvvMBTfkmk+srWI1xNpTxdqJNna85DVk8PBmL6MrqyIiIiKSLBWrIiIiIpIsFasiIiIikiwVqyIiIiKSLBWrIiIiIpIsFasiIiIikiwLIRQ9BxERERGRzdKVVRERERFJlopVEREREUmWilURERERSZaKVRERERFJlopVEREREUlWxYtVM2trZq0385iVvh/YhDHGmdlWlZ5bEZSPhpSPhpSPhpSPhpSPhpSPhpSPhpSPhv6V8lFW6yoz6w7cC7wDdATeBWYBRwG7AmuAJcDWwOdL3/8GWA3cD5xV+r49cAdwAxBKz+seckzOzD4GXAxsA1wXQrgv9wY2f93J5aPe3EYBR4UQRuYdI8c6k8uHmc0HOpf+uy6E0DPn5jVbivkozWt74Fng6BDCq/m2Ltd6k8qHmX0LGFvvoW2B/w4h/Fe+LWye1PJRmtMlwNml/34nhHBbzs1rttTyYWZtgR8DnwLeBsaEEB4uayObt/6k8lGa0znAZcAbwCkhhIW5N7D5604uH6V5jQD+PYQwKu+25VxvcvkwswuBi0rjXBFCuCb3BoYQyvoCOgH7AI8D/es9/mNgyGae3woYBHwNGAlsV/r3QmAycATwRhlzWQAcAxwNvAl0LncbP6r5qLeOnYDlwO3VzEVq+SA7COdVOwep5qPeOn4C/ED5+NC6HgEO2FLzAewLPAi0JnuxexPougXn42vAJGAr4BDgr0DHLTgf+wGLge5Af+B31cxFavkojf9p4B8U8FqbWj5K+8QzwO6lOb0D7Jp32ypxG0BPspP670IIj5vZgWb2GPA54Aoze8TMrgMws27AsSGEB+stX1dKMMBaYF0Zc9kFuDiEMDOE8BDwl9L8qimlfGw0HphRgXHySCkfBwDPlbF8JaSUD8zsIOAk4HvljFOGpPKxkZl9AvhbCOGZSozXDCnlY29gbghhfQjhL8DrQLcyxssjpXwMByaGENaFEOaSXQDoV8Z4eaSUj6HAHSGEJSGEx4EuZtahjPHySCkfAKOAK8ocoxwp5WM1cFoI4dUQwjzgNbJfbHIp6z6E0n0ML5LttF1KD7cFXgX+nSxxrYAflGLtgP82s171hnmfrOJuF1nPBOBPIYQ7YvMJIbwAvFC6R2Mo0AaY15xtKkdq+Sg9d3BpvZcDX2z61pQvwXwcBBxhZkuA94CxIYT/bcYmlSXBfABMKI03yczuA24MpV+LW1qi+djoPCD/W1Y5JJiPecB/lV7c+pTmNL/JG1SmBPPBxnmYWUdgN7KCtSoSzEdP4KF6/19ClpOqvOYmmA+Az1Ll19mNUstH/V/0S7co7Ao83+QN2kS5V1Y/QVbFTwa+a2ZvkV36heyy749L3weAkN3PMgiYXW+M9RvjnhDCuc18oTmX7O2am0II65uxXLmSyoeZtQOuBb7c2JgtJKl8kN3D860QQndgBPA/ZrZ1E7elEpLKh5kNAAYAN5Hdo3Q+8JWmbkwFJJWPjcysJ7B7CGFWU5epkNTyMY/s3anfAbcCPwwhrGnaplREavmYDPykdG/zDOCFEMJrTduUikgtH63JCpuNVgHbN2G5SkktH1TrF31Hcvmo53tk9diqZi73f8oqVkMIvw8hHE6WnHHAK8CfS+EOZBX9psv8DTiW7MMLVi9UsU+bhRCuBfYExplZn0qN24T1ppaPS4E7QwgvVWCsZkstHyGEO0IIk0rfzyZ7W/OAcsdtxvqTygdwGPCbEML4EMKvyN6+OqEC4zZJgvnY6DTg7gqO1yQJ5mMksDCEcDDZ291DSx9grYrU8hFCuBH4BtlnAA7mn1eoqiK1fJB9yGz7ev9vB2yowLhNkmA+CpVqPszseLJ7X8u6PaKiratCCIeSfeIMoJYsWVBvw82sP3AG2Q3Archu0P4T2dsJrcneus/FzPqY2f6lubwBPEn17yn6P0Xng6zwON/MlgL3AMPN7J4yxitL0fkwszNKn+jdqAdVPLluquh8kF0VWVTv/++TfTigEAnkY6OTgWkVGKcsCeTjcEpv6YYQNgBPk32wqBAJ5IMQwlSyT1jXkZ1TC5NAPuZSunJnZkZWwC8uY7yyJJCPpKSQDzPbFbgFGFHOVVVomT8KsIysHcK/k30y/z3gp/Xi55O1MJhFdmn6eeASskvRd7CZ6r8ZugN3mdn2ZrYL8DHgqTLGq4TC8hFCqA0hdA0hdCP7EM1dIYST8o5XIUXuH0eSFe/tzeyrZAdy0R+4KjIfjwDHmdlOpVtGziJ7IS5SkfnAzHYEeocQni1nnAoqMh8LyX7BPcDMjgZOpYx7ziqk0P2j5CJgXMFv+W5UZD7uA4aZ2UnAd4C3QvZBvCKlsH+kpLB8lG6x+1/g6hDCnLzjbFSpYnUb/nkfxGul/+9Edo/gVEo35Zc+7HMAMMXM+pF9Qu0Ssis6/xVCmBFCWMwm1byZTTCzMxqbRAjhj2QJng88DJwXQlhSiQ1spiTykZBU8jEOOI7sAP4c8JkQwgdlb13zJZGPEMKLZPcSPUrWJ3ExDU9k1ZJEPkqOBMo+sZYplXxcT7ZP/BH4FXBbqGJf0XpSyQdmNgRYE6rYv3szkshHCOEdsg8UfZOsl+eISmxcDknkIyGp5OM44EDgQjNbWvo6OfdWhfL7ek0luzrVk+z+lSlk1XTnUnx/4AVgj1JiPlt6/EHg1NL3nYEfkRXPdcDMcudV1JfyoXwoH8qH8qF8KB/Kh/JRua+y/oLV5pjZ3iGE+Zs8tlPIbuSt/1ibEMLazSzfPoSwuqKTKpDy0ZDy0ZDy0ZDy0ZDy0ZDy0ZDy0ZDy0dC/Uj4qXqyKiIiIiFRKS3zASkRERESkIlSsioiIiEiyVKyKiIiISLKif6VgcKtTct3QunTsADc2fNSDbuyl1TtHx112Znc3tr7uxcYnthkzNkyxxp+VyZuPmNXDDnNjP73u2uiy5w35khtLOR+jX3rZjU1/66A8QwKwZ/s33djMr/v7ZKtZfiveauSjdVd/v3/9hp3yDAlAz5Prci/raU4+IH9ONhzl7wdfvWWKGzv/t6dHx912kf/7+Xav+X+Zuf3UJ9xYNfaRRZf5++/0UVe7sXlrukbHvfQGvwNNt/GPNT6xzSg6H1eN8P8S5NiHPx8dt++X5uaZTlQ18hE7Xo75cb6fI+Q/b8ZUIx8PLHnaje1x1zlurM/Y2W6spVQqH7Fa4uHrb3JjC9b6vfrPO3RYdD7rl/mvuXl5+dCVVRERERFJlopVEREREUmWilURERERSZaKVRERERFJlopVEREREUmWilURERERSVa0dVVeazr5sTvvGeTGWu23IjruoT9d5MaWHN7otAoTa00UaynR+/5zo+P2rat8m5VqGNrhXTd2/m9rc4/750X7ubEeL/jtsvyGRZUT2weOmPG6G3v9Jb911W5j/ubGoDrb1VLWXfx3Nxbbf4YOvzH3OqMtXB6Lt3CphLdH9ndj88++wY0dOu4/3NiqE96JrrPuAn/cga99xY3FWnlVSuyYiedjtBtr30g+Yi2xai7L3wKqpeVt59Z2Rfx61fQ7/LZosbZGLdHSaFOta/tFok+7kTMHzXRjs2iXf0IFW3Kk3wFr2qpt3djQDv6YKwf0jq6z/dSW/zlvpCurIiIiIpIsFasiIiIikiwVqyIiIiKSLBWrIiIiIpIsFasiIiIikiwVqyIiIiKSrBZpXRVr8RFrR9J1+nvRcR+a57eq6Eu6bZzmX1XjxmLtcva+yG/VBfDejN3c2Gt/8Vse9f1SsbmKtdGItVLp+FqIjtv5dn+/K7qNU6wFyD7t/Hn3PNlvpVL0NpVrw1EHubGZtbe5scGnjnJjbZavjq7zuntvdWOnXHeBG+u2rOXbFn3Q2W89EztPdHnSb/O16oT4KT52LHZ8bKEbq8a+F2t/dOVy/7Uglo9OC9tH13nMjx90Y7MuK7atUex4GdrhaTc2cezs3Ov8ZI3fPnG709q6sW7jq9fSqLnGdXnRjd01dkx02W7j021ftuMz/vnjfPz2ZeW0+6smXVkVERERkWSpWBURERGRZKlYFREREZFkqVgVERERkWSpWBURERGRZKlYFREREZFktUjrqpiFo/u4sXtrboguu/f9+1V6OhUTaysyfuAv3djx95zvxvosi7ccObfXy27s2++cGF22SPu0XebGpo+62o31bdMhOu6QJ4e7sfV1fruSaninV2s3Nv0tf9+BlZWfTCJW9N7Gje1x1zlurM8s/7iYf+sh0XXOW9PVjRXdlqbHz/3j+VejDnZj9864y43FWlMB3HzEADcWax1VtGnXfsKNTbr3h26ssXNIzCNdj3Vj1cjVmwf7rbNirbzK0fM3/nlryZEbWmSdTRU7p8fOH69EWjWtO2JFfKXjG51WYTrf/rgfiyw3qv9AN7bkSL8dFkCfqY3NqnJ0ZVVEREREkqViVURERESSpWJVRERERJKlYlVEREREkqViVURERESSpWJVRERERJJV9dZVNZf57WEOfW10dNmrvn2HG7t5YrEtWJZ8Y40bG9rhXTc2vX+dP+jsjtF1xsY9f1F82SL9aoXfhmfm1/2fY6x1C8C7Z/mtVPqMbXxeLendGn9ur63cwY2tGrmvHzvhneg6d97O3z/anf6eG6tWy6JYq5WVl/n7wYJIe6oHBk2IrjO2760edpgbaz/1iei4lRDL++SX4i25PJ/t9Odo/MULd3djfcam27oqtu98/faPu7G3R/aPjhs7pjaM7uTGai5r+Vxt99p6N/Y/c/32Q32Z2xLTSVqfsX57u2mf9tu5HdpjUXTcJblnVKzWXXd2Yxft4vefmr0inXahurIqIiIiIslSsSoiIiIiyVKxKiIiIiLJUrEqIiIiIslSsSoiIiIiyVKxKiIiIiLJUrEqIiIiIsnK3Wc11rdr4eg+bizWZzXWOw/gol5nuLFukxa7sbaDo8NWxIg98/WyO3HHp9zYPm2XNbJ0Bzey4zOWaz7VMGt/v19qK/x87MxB0XGHj/L3rVnEe7S2tNjPY+bw6W7sym/2c2N33TYous43j/Bj71+4ixtLob9mq/1WuLGF/SdHlvSPCYj3HZ35d7+3a9GmHnKzGztvyJfc2Mwu8W26745r3Ngpiy5wY93G+8dayhp7jenypH+8nT3N7/M98TL/Na9SOj620I19+fLX3Vg5574POsWuZ4Xc4xbp28+e6MbqoucWGFI73I2tr3sx95xa2vLj93Bjfdv458yeM/1+3ABLx/rnl3f283vP9/1S8+slXVkVERERkWSpWBURERGRZKlYFREREZFkqVgVERERkWSpWBURERGRZKlYFREREZFk5W5dFTP/7Bvc2MBnvuLG2k99Ijru+zV+K4SixdoxzeLAXGP++PV466orl/ttVhpr0VKkt0f2d2Nbr9jgxpYcGW/H1XO1306tdVf/57N+Wcu3aor9PGpPGOHGVq/w5923kfZBizr5bUVqPu63ektBz5Pr3NiVz/r7/Z33xNt5xVrnxdqmpWxtl/Zu7NWhW1dxJtWzethhbqzjgn+4scbaC+VtP7ThqHhbvUqInaf2aecfz3de5rd8jB0PAB1GLHFjW9/QLbpskWKvMYf2eD73uO9d974bW/T84W5s92kf5F5nU8X2wUmX/9CNLVjrj9nY+ePMQQ+6sVibwK/z8ei4m6MrqyIiIiKSLBWrIiIiIpIsFasiIiIikiwVqyIiIiKSLBWrIiIiIpIsFasiIiIikqzcratibTQOHTfajQ29/A9u7KVv+a2HAB6oucWNDT51VGTJ16PjFinWgqVvm6ejy/7P3IH+sszNO6WKaN3V/1nG2mjEfGXBadH4G9/q48ZaLUu3LdFuY/7mxrpOf8+N3bbk6ei401a97MZuHvopN7Y+Omp1LB3rt936bKer3dgjUw6MjpvCtuVxynUXuLFnfuG3Cjym7sTouGee90031m1qvK1RkT7o5F9n+em9t7qxvm065F7nqEX++bbNC8W2grv+rFPc2PQ7/OOl79nxfMTOIROn+ufbotWe47enuq3m4dzjzqydHlmpH+rd6azc62yq93dom2u52DHxyvAb806HvW/2z1k1NP/coiurIiIiIpIsFasiIiIikiwVqyIiIiKSLBWrIiIiIpIsFasiIiIikiwVqyIiIiKSLAshFD0HEREREZHN0pVVEREREUmWilURERERSZaKVRERERFJlopVEREREUmWilURERERSZaKVRERERFJ1v8HprPtqKA0QzcAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 864x504 with 50 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn import datasets, metrics, svm\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "digits = datasets.load_digits()\n",
        "data = digits.images.reshape((len(digits.images), -1))\n",
        "X_train, X_test, y_train, y_test = train_test_split(data, digits.target, test_size=0.5, shuffle=False)\n",
        "\n",
        "clf = svm.SVC(gamma=0.001)\n",
        "clf.fit(X_train, y_train)  # 训练\n",
        "predicted = clf.predict(X_test)  # 预测\n",
        "\n",
        "figure = plt.figure(figsize=(12, 7))\n",
        "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文\n",
        "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
        "\n",
        "with plt.style.context('Solarize_Light2'):\n",
        "\n",
        "    for i in range(30):\n",
        "        ax = plt.subplot(5, 10, i+1)\n",
        "        ax.set_axis_off()\n",
        "        ax.imshow(digits.images[i], interpolation='nearest')\n",
        "        ax.set_title('训练: %i' % digits.target[i], fontdict={'fontsize': 12})\n",
        "\n",
        "    for i in range(20):\n",
        "        ax = plt.subplot(5, 10, i+31)\n",
        "        ax.set_axis_off()\n",
        "        ax.imshow(X_test[i].reshape(8, 8), interpolation='nearest')\n",
        "        ax.set_title('预测: %i' % predicted[i], fontdict={'fontsize': 12})\n",
        "\n",
        "print(f\"{clf}的分类结果\\n\"\n",
        "      f\"{metrics.classification_report(y_test, predicted)}\\n\")\n",
        "\n",
        "disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, predicted)\n",
        "disp.figure_.suptitle(\"混淆矩阵\")\n",
        "print(f\"混淆矩阵\\n{disp.confusion_matrix}\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    }
  ],
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